Project Summary Lower urinary tract symptoms (LUTS) are costly to treat and significantly affect patients' quality of life. The identification of subtypes of LUTS is critical to the understanding of LUTS pathophysiology and effective clinical management and treatment of patients. Novel tools that can accurately identify the presence, types, and severity of LUTS are needed, and biological markers are one such type of tool. Recent systematic review of biomarkers of LUTS demonstrated the existence of important knowledge gap in published research: poor reproducibility, unclear classification of LUTS patients, and lack of adjustments for clinical covariates. The Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) was established with the goal of identifying and explaining clinically relevant subtypes of LUTS patients. Over 75,000 biosamples were collected and stored at NIDDK repository, including over 1200 baseline plasma samples for patients and controls, as well as serum samples collected longitudinally for more than 800 patients. We propose to analyze plasma and serum samples collected in LURN to identify biomarker signatures of subtypes of LUTS and their response to treatments. We hypothesize that: (a) subtypes of LUTS will have different biomarker signatures of differentially abundant proteins; (b) biomarker signatures will differ not only across the subtypes of LUTS but also in comparison of improvers and non-improvers; (c) longitudinal changes in biomarker signatures in response to treatment will be different in improvers and non-improvers. Our study consists of three specific aims. Aim 1. To identify protein biomarker signatures contained within plasma of subgroups of men and women with LUTS. We propose to analyze a panel of 1306 proteins using the highly sensitive and reproducible SomaScan assay, which we successfully used in the Biomarker Pilot Project in LURN I. This aim serves to produce comprehensive phenotyping of LUTS by identifying differentially abundant proteins among patients with different subtypes of LUTS. We will build upon the results of LURN I and use biomarker data to refine our previously identified symptom-based subtypes of men and women with LUTS. This is important for better classification, diagnosis, and personalized treatment of patients with LUTS. Aim 2. To identify biomarkers in baseline plasma samples associated with improvement in LUTS. We will use the results of assay performed in Aim 1 to test for significant differences between the biomarker signatures of ?best improvers? and ?worst non-improvers?. This is of practical importance since it will facilitate early selection of patients who may benefit from more intensive or alternative treatments to LUTS. Aim 3. To quantify longitudinal changes in biomarker signatures within serum of ?best improvers? and ?worst non-improvers? among men and women with LUTS. Combined with the existing biological knowledge of metabolic and signaling pathway using enrichment analysis this will allow identifying pathways correlated with the improved symptoms and will help in understanding of LUTS pathophysiology and development of better treatments.